CRAN packages for generalized linear models and with related methods

Packages for glm’s and associated methods

The current vignette is a quick analysis of all CRAN packages that have “glm” in their name. The asssumption here is that a package with “glm” in its name most probably does something related to generalized linear models.

Preparing today’s CRAN package database

Download today’s CRAN database and clean and organise author names, depends, imports, suggests, enhances.

library("cranly")
p_db <- tools::CRAN_package_db()
package_db <- clean_CRAN_db(p_db)

Let’s build the CRAN package directives and collaboration networks

package_network <- build_network(package_db)
author_network <- build_network(package_db, perspective = "author")

Packages for generalized linear models

The packages that have “glm” in their name are

(glm_packages <- package_with(package_network, name = "glm"))
#>  [1] "bestglm"         "bglm"            "biglm"          
#>  [4] "brglm"           "brglm2"          "circglmbayes"   
#>  [7] "CompGLM"         "CPMCGLM"         "designGLMM"     
#> [10] "dglm"            "dhglm"           "EBglmnet"       
#> [13] "ezglm"           "geoRglm"         "glm.ddR"        
#> [16] "glm.deploy"      "glm.predict"     "glm2"           
#> [19] "GLMaSPU"         "glmbb"           "glmBfp"         
#> [22] "glmc"            "glmdm"           "glmertree"      
#> [25] "glmgraph"        "glmlep"          "glmm"           
#> [28] "glmmADMB"        "glmmBUGS"        "glmmLasso"      
#> [31] "glmmML"          "GLMMRR"          "glmmsr"         
#> [34] "glmmTMB"         "glmnet"          "glmnetcr"       
#> [37] "glmnetUtils"     "glmpath"         "glmpathcr"      
#> [40] "glmtlp"          "glmulti"         "glmvsd"         
#> [43] "glmx"            "HBglm"           "HDGLM"          
#> [46] "hglm"            "hglm.data"       "HiCglmi"        
#> [49] "icdGLM"          "lsplsGlm"        "mbrglm"         
#> [52] "mcemGLM"         "mcglm"           "MCMCglmm"       
#> [55] "mdhglm"          "MGLM"            "mglmn"          
#> [58] "misclassGLM"     "mvglmmRank"      "oglmx"          
#> [61] "pglm"            "plsRglm"         "poisson.glm.mix"
#> [64] "QGglmm"          "r2glmm"          "randomGLM"      
#> [67] "simglm"          "speedglm"        "StroupGLMM"

and the corresponding subnetworks are

glm_package_network <- subset(package_network, package = glm_packages)
glm_author_network <- subset(author_network, package = glm_packages)

As the following visualizations illustrate these networks are heavily connected

visualize(glm_package_network, package = glm_packages, title = TRUE)

visualize(glm_author_network, package = glm_packages, title = TRUE)

The top-20 packages in terms of various statistics of the directives sub-network for generalized linear models are

glm_package_summaries <- summary(glm_package_network)
plot(glm_package_summaries, according_to = "degree")

plot(glm_package_summaries, according_to = "betweenness")

plot(glm_package_summaries, according_to = "page_rank")

The top-20 in the collaboration sub-network for generalized linear models are

glm_author_summaries <- summary(glm_author_network)
plot(glm_author_summaries, according_to = "degree")

plot(glm_author_summaries, according_to = "betweenness")

plot(glm_author_summaries, according_to = "page_rank")